Fuzzy multi-level classifier for medical applications.
In this paper a fuzzy pattern recognition model is described, which is a tool to handle problems with noncrisp and multi-class membership of the objects. It is oriented to medical diagnostics, where the patients suffer from more than one disease in different degrees. Fuzzy pattern recognition is supposed to fit medical diagnostic problems better than conventional pattern recognition. The design of a multi-level fuzzy decision scheme is considered in order to derive high performance, taking into account expert logic and human experience. Two main topics are discussed--the criterion for evaluation of classification accuracy and the training rule. The implementation of fuzzy multi-level classifier is illustrated with real clinical data.
Kuncheva
L I
LI
Department of Biomedical Cybernetics, Bulgarian Academy of Sciences, Sofia.
D000337:Aerospace Medicine; D001794:Blood Pressure / Q000502:physiology; D003936:Diagnosis, Computer-Assisted; D005103:Expert Systems; D006801:Humans; D000860:Hypoxia / Q000503:physiopathology; D008297:Male; D008991:Monitoring, Physiologic; D010363:Pattern Recognition, Automated; D011674:Pulse / Q000502:physiology